{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aplication of Multi Dimensional LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
     ]
    }
   ],
   "source": [
    "%reset\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys\n",
    "from time import time\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow.contrib.slim as slim\n",
    "from tensorflow.python.ops.rnn import dynamic_rnn\n",
    "\n",
    "from data_gen import next_batch\n",
    "from md_lstm import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set varables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LTSM cells to use\n",
    "TYPE = 'MD_LSTM'\n",
    "# Number of (MD-) LSTM units to use\n",
    "hidden_size = 4\n",
    "\n",
    "# Total number of steps to train on\n",
    "steps = 100000\n",
    "# Show error N steps\n",
    "show_error_iter = 1000\n",
    "# Show test image every N steps\n",
    "show_img_iter = 10000\n",
    "\n",
    "# Size of the training batch\n",
    "batch_size = 16\n",
    "\n",
    "# ADAM learning rate\n",
    "learning_rate = 0.001 * 0.5\n",
    "\n",
    "# Input image (generated random field) dimensions\n",
    "h = 32\n",
    "w = 32\n",
    "# Number of chanels of the image (only one, module of the field)\n",
    "channels = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Network Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Multi Dimensional LSTM !\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Check analysis type\n",
    "if TYPE == 'MD_LSTM':\n",
    "    use_multi_dimensional_lstm = True\n",
    "elif TYPE == 'LSTM':\n",
    "    use_multi_dimensional_lstm = False\n",
    "else:\n",
    "    error('Unknown analysis type')\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "# Build network\n",
    "\n",
    "# Input placeholder\n",
    "x = tf.placeholder(tf.float32, [None, h, w, channels])\n",
    "# Output placeholder\n",
    "y = tf.placeholder(tf.float32, [None, h, w, channels])\n",
    "\n",
    "\n",
    "\n",
    "# Create chosen LSTM layer type\n",
    "if use_multi_dimensional_lstm:\n",
    "    print('Using Multi Dimensional LSTM !')\n",
    "    rnn_out, _ = multi_dimensional_rnn_while_loop(rnn_size=hidden_size, input_data=x, sh=[1, 1])\n",
    "else:\n",
    "    print('Using Standard LSTM !')\n",
    "    rnn_out = standard_lstm(input_data=x, rnn_size=hidden_size)\n",
    "\n",
    "\n",
    "# The output of the (MD_)LTSM layer is connected to a fully connected layer to generate output predction    \n",
    "model_out = slim.fully_connected(inputs=rnn_out,\n",
    "                                 num_outputs=1,\n",
    "                                 activation_fn=None)\n",
    "\n",
    "# Using the model output we construct the loss function to be optimized \n",
    "loss = 1e4 * tf.reduce_mean(tf.abs(tf.subtract(y, model_out)))\n",
    "\n",
    "# Finally we attach an optimizer\n",
    "grad_update = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start Session and initialize graph\n",
    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# Add tensorboard (Really usefull)\n",
    "train_writer = tf.summary.FileWriter('Tensorboard_out' + '/MDLSTM',sess.graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 000 | loss = 8715.301 | time 3.384\n"
     ]
    },
    {
     "data": {
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8/KjHprF0YmV6YLxQnMG0w3iZMOXlha6rPHRH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\nQhYEJEmSJElqof8Hf1CUT1/3KrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b3832fc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 1000 | loss = 951.276 | time 1.083\n",
      "steps = 2000 | loss = 650.888 | time 1.066\n",
      "steps = 3000 | loss = 524.367 | time 1.040\n",
      "steps = 4000 | loss = 506.820 | time 1.029\n",
      "steps = 5000 | loss = 509.525 | time 1.060\n",
      "steps = 6000 | loss = 476.272 | time 1.048\n",
      "steps = 7000 | loss = 371.745 | time 1.032\n",
      "steps = 8000 | loss = 269.103 | time 1.031\n",
      "steps = 9000 | loss = 293.208 | time 1.078\n",
      "steps = 10000 | loss = 341.213 | time 1.022\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b4307da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 11000 | loss = 247.470 | time 1.065\n",
      "steps = 12000 | loss = 283.844 | time 1.031\n",
      "steps = 13000 | loss = 269.268 | time 1.024\n",
      "steps = 14000 | loss = 249.194 | time 1.045\n",
      "steps = 15000 | loss = 216.143 | time 1.050\n",
      "steps = 16000 | loss = 237.529 | time 1.026\n",
      "steps = 17000 | loss = 399.623 | time 1.031\n",
      "steps = 18000 | loss = 435.889 | time 1.076\n",
      "steps = 19000 | loss = 335.151 | time 1.025\n",
      "steps = 20000 | loss = 301.941 | time 1.024\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b04de9110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 21000 | loss = 319.192 | time 1.062\n",
      "steps = 22000 | loss = 318.195 | time 1.023\n",
      "steps = 23000 | loss = 308.684 | time 1.048\n",
      "steps = 24000 | loss = 310.307 | time 1.077\n",
      "steps = 25000 | loss = 297.116 | time 1.033\n",
      "steps = 26000 | loss = 298.447 | time 1.039\n",
      "steps = 27000 | loss = 289.856 | time 1.025\n",
      "steps = 28000 | loss = 289.438 | time 1.040\n",
      "steps = 29000 | loss = 265.351 | time 1.050\n",
      "steps = 30000 | loss = 254.987 | time 1.057\n"
     ]
    },
    {
     "data": {
      "image/png": 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jeSzu2FTeZl3MF7fZ2KANwM658m3G9M7y/YOZ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\nA63YQ5ULQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b05144890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 31000 | loss = 232.396 | time 1.027\n",
      "steps = 32000 | loss = 223.411 | time 1.021\n",
      "steps = 33000 | loss = 214.167 | time 1.032\n",
      "steps = 34000 | loss = 217.837 | time 1.060\n",
      "steps = 35000 | loss = 176.599 | time 1.022\n",
      "steps = 36000 | loss = 195.256 | time 1.046\n",
      "steps = 37000 | loss = 183.314 | time 1.067\n",
      "steps = 38000 | loss = 184.964 | time 1.042\n",
      "steps = 39000 | loss = 178.755 | time 1.058\n",
      "steps = 40000 | loss = 180.745 | time 1.066\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q/vcYS/vvT3z15Of8GfyhlNKDc+g7AACYDfkeAIA1tciC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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b05144310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 41000 | loss = 189.909 | time 1.071\n",
      "steps = 42000 | loss = 177.679 | time 1.034\n",
      "steps = 43000 | loss = 181.121 | time 1.080\n",
      "steps = 44000 | loss = 187.401 | time 1.027\n",
      "steps = 45000 | loss = 177.378 | time 1.024\n",
      "steps = 46000 | loss = 164.339 | time 1.044\n",
      "steps = 47000 | loss = 170.166 | time 1.053\n",
      "steps = 48000 | loss = 150.118 | time 1.029\n",
      "steps = 49000 | loss = 153.148 | time 1.054\n",
      "steps = 50000 | loss = 157.857 | time 1.091\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b04f59850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 51000 | loss = 150.777 | time 1.033\n",
      "steps = 52000 | loss = 139.633 | time 1.036\n",
      "steps = 53000 | loss = 133.205 | time 1.022\n",
      "steps = 54000 | loss = 140.805 | time 1.024\n",
      "steps = 55000 | loss = 131.522 | time 1.033\n",
      "steps = 56000 | loss = 138.879 | time 1.071\n",
      "steps = 57000 | loss = 132.119 | time 1.030\n",
      "steps = 58000 | loss = 133.363 | time 1.044\n",
      "steps = 59000 | loss = 134.047 | time 1.022\n",
      "steps = 60000 | loss = 127.285 | time 1.029\n"
     ]
    },
    {
     "data": {
      "image/png": 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9LsNviTF+ag+e+v2Srk4iHrrZgtcTYzx9sy9J/AUCAIAJ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\nFrIgIEmSJElSC/0HLwzH3tkxLS8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b04e98850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 61000 | loss = 134.032 | time 1.029\n",
      "steps = 62000 | loss = 137.206 | time 1.635\n",
      "steps = 63000 | loss = 130.330 | time 1.625\n",
      "steps = 64000 | loss = 134.150 | time 1.449\n",
      "steps = 65000 | loss = 128.933 | time 1.524\n",
      "steps = 66000 | loss = 138.345 | time 1.233\n",
      "steps = 67000 | loss = 135.909 | time 1.255\n",
      "steps = 68000 | loss = 124.980 | time 1.198\n",
      "steps = 69000 | loss = 127.541 | time 1.168\n",
      "steps = 70000 | loss = 127.415 | time 1.053\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b04a067d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 71000 | loss = 128.872 | time 1.082\n",
      "steps = 72000 | loss = 123.234 | time 1.064\n",
      "steps = 73000 | loss = 128.372 | time 1.035\n",
      "steps = 74000 | loss = 130.793 | time 1.046\n",
      "steps = 75000 | loss = 125.618 | time 1.095\n",
      "steps = 76000 | loss = 131.443 | time 1.099\n",
      "steps = 77000 | loss = 125.501 | time 1.068\n",
      "steps = 78000 | loss = 125.095 | time 1.051\n",
      "steps = 79000 | loss = 133.006 | time 1.048\n",
      "steps = 80000 | loss = 132.862 | time 1.033\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7afc7d2c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 81000 | loss = 126.861 | time 1.021\n",
      "steps = 82000 | loss = 129.944 | time 1.077\n",
      "steps = 83000 | loss = 129.841 | time 1.061\n",
      "steps = 84000 | loss = 124.443 | time 1.085\n",
      "steps = 85000 | loss = 129.226 | time 1.060\n",
      "steps = 86000 | loss = 132.687 | time 1.036\n",
      "steps = 87000 | loss = 128.093 | time 1.049\n",
      "steps = 88000 | loss = 126.988 | time 1.033\n",
      "steps = 89000 | loss = 125.178 | time 1.173\n",
      "steps = 90000 | loss = 128.503 | time 1.034\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMZ652Zck/gIBAMCUDuN8z1wPAMA3OrCCQAjhDkn/TdId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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b04088e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps = 91000 | loss = 126.753 | time 1.052\n",
      "steps = 92000 | loss = 129.270 | time 1.047\n",
      "steps = 93000 | loss = 129.305 | time 1.077\n",
      "steps = 94000 | loss = 122.897 | time 1.039\n",
      "steps = 95000 | loss = 122.366 | time 1.094\n",
      "steps = 96000 | loss = 124.368 | time 1.041\n",
      "steps = 97000 | loss = 126.335 | time 1.041\n",
      "steps = 98000 | loss = 133.620 | time 1.036\n",
      "steps = 99000 | loss = 127.179 | time 1.049\n",
      "steps = 100000 | loss = 128.082 | time 1.078\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b04d71250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate test sample to show improvements (a batch of 1 sample)\n",
    "batch = next_batch(1, h, w, anisotropy = False)\n",
    "# Expand to generate the channels dimension\n",
    "test_x = np.expand_dims(batch[0], axis=3)\n",
    "test_y = np.expand_dims(batch[1], axis=3)\n",
    "\n",
    "# Train\n",
    "for i in range(steps+1):\n",
    "    \n",
    "    # Generate gaussian random field as imput data\n",
    "    batch = next_batch(batch_size, h, w, anisotropy = False)\n",
    "    \n",
    "    # Get current time (to measure train step time)\n",
    "    st = time()\n",
    "    \n",
    "    # Expand to generate the channels dimension\n",
    "    batch_x = np.expand_dims(batch[0], axis=3)\n",
    "    batch_y = np.expand_dims(batch[1], axis=3)\n",
    "\n",
    "    # If the LTMS is not Multi Dimensional we shuffle the width\n",
    "    if not use_multi_dimensional_lstm and i == 0:\n",
    "        print('Shuffling the batch in the height dimension for the standard LSTM.'\n",
    "              'Its like having h LSTM on the width axis.')\n",
    "        perms = np.random.permutation(list(range(w)))\n",
    "        batch_x = batch_x[:, perms, :, :]\n",
    "        batch_y = batch_y[:, perms, :, :]\n",
    "        pass\n",
    "\n",
    "    # Train the network and the loss value\n",
    "    loss_val, _ = sess.run([loss, grad_update], feed_dict={x: batch_x, y: batch_y})\n",
    "    \n",
    "    # Print step information if is the chosen step\n",
    "    if i%show_error_iter == 0:\n",
    "        print('steps = {0} | loss = {1:.3f} | time {2:.3f}'.format(str(i).zfill(3),\n",
    "                                                               loss_val,\n",
    "                                                               time() - st))\n",
    "    # Plot example if is the chosen step\n",
    "    if i%show_img_iter == 0:\n",
    "        # Test current Network\n",
    "        prediction_data = sess.run([model_out], feed_dict={x: test_x})\n",
    "        prediction_data_np = np.array(prediction_data)\n",
    "        \n",
    "        # Plot\n",
    "        fig = plt.figure(dpi=200)\n",
    "        ax = plt.subplot(121)\n",
    "        ax.imshow(test_y[0,:,:,0])\n",
    "        ax = plt.subplot(122)\n",
    "        ax.imshow(prediction_data_np[0,0,:,:,0])\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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